Run the MapReduce examples included in HDInsight

Article
09/14/2023

Linux cluster

Learn how to run the MapReduce examples included with Apache Hadoop on HDInsight.

Prerequisites

An Apache Hadoop cluster on HDInsight. See Get Started with HDInsight on Linux.
An SSH client. For more information, see Connect to HDInsight (Apache Hadoop) using SSH.

The MapReduce examples

The samples are located on the HDInsight cluster at /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar. Source code for these samples is included on the HDInsight cluster at /usr/hdp/current/hadoop-client/src/hadoop-mapreduce-project/hadoop-mapreduce-examples.

The following samples are contained in this archive:

Sample	Description
aggregatewordcount	Counts the words in the input files.
aggregatewordhist	Computes the histogram of the words in the input files.
`bbp`	Uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount	Counts the pageview logs stored in a database.
distbbp	Uses a BBP-type formula to compute exact bits of Pi.
grep	Counts the matches of a regex in the input.
join	Performs a join over sorted, equally partitioned datasets.
multifilewc	Counts words from several files.
pentomino	Tile laying program to find solutions to pentomino problems.
pi	Estimates Pi using a quasi-Monte Carlo method.
randomtextwriter	Writes 10 GB of random textual data per node.
`randomwriter`	Writes 10 GB of random data per node.
`secondarysort`	Defines a secondary sort to the reduce phase.
sort	Sorts the data written by the random writer.
sudoku	A sudoku solver.
teragen	Generate data for the terasort.
terasort	Run the terasort.
teravalidate	Checking results of terasort.
wordcount	Counts the words in the input files.
`wordmean`	Counts the average length of the words in the input files.
`wordmedian`	Counts the median length of the words in the input files.
wordstandarddeviation	Counts the standard deviation of the length of the words in the input files.

Run the wordcount example

Connect to HDInsight using SSH. Replace CLUSTER with the name of your cluster and then enter the following command:
```
ssh sshuser@CLUSTER-ssh.azurehdinsight.net
```
From the SSH session, use the following command to list the samples:
```
yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar
```
This command generates the list of sample from the previous section of this document.
Use the following command to get help on a specific sample. In this case, the wordcount sample:
```
yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar wordcount
```
You receive the following message:
```
Usage: wordcount <in> [<in>...] <out>
```
This message indicates that you can provide several input paths for the source documents. The final path is where the output (count of words in the source documents) is stored.
Use the following to count all words in the Notebooks of Leonardo da Vinci, which are provided as sample data with your cluster:
```
yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar wordcount /example/data/gutenberg/davinci.txt /example/data/davinciwordcount
```
Input for this job is read from /example/data/gutenberg/davinci.txt. Output for this example is stored in /example/data/davinciwordcount. Both paths are located on default storage for the cluster, not the local file system.

Note

As noted in the help for the wordcount sample, you could also specify multiple input files. For example, hadoop jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar wordcount /example/data/gutenberg/davinci.txt /example/data/gutenberg/ulysses.txt /example/data/twowordcount would count words in both davinci.txt and ulysses.txt.
Once the job completes, use the following command to view the output:
```
hdfs dfs -cat /example/data/davinciwordcount/*
```
This command concatenates all the output files produced by the job. It displays the output to the console. The output is similar to the following text:
```
zum     1
zur     1
zwanzig 1
zweite  1
```
Each line represents a word and how many times it occurred in the input data.

The Sudoku example

Sudoku is a logic puzzle made up of nine 3x3 grids. Some cells in the grid have numbers, while others are blank, and the goal is to solve for the blank cells. The previous link has more information on the puzzle, but the purpose of this sample is to solve for the blank cells. So our input should be a file that is in the following format:

Nine rows of nine columns
Each column can contain either a number or ? (which indicates a blank cell)
Cells are separated by a space

There is a certain way to construct Sudoku puzzles; you can't repeat a number in a column or row. There is an example of the HDInsight cluster that is properly constructed. It is located at /usr/hdp/*/hadoop/src/hadoop-mapreduce-project/hadoop-mapreduce-examples/src/main/java/org/apache/hadoop/examples/dancing/puzzle1.dta and contains the following text:

8 5 ? 3 9 ? ? ? ?
? ? 2 ? ? ? ? ? ?
? ? 6 ? 1 ? ? ? 2
? ? 4 ? ? 3 ? 5 9
? ? 8 9 ? 1 4 ? ?
3 2 ? 4 ? ? 8 ? ?
9 ? ? ? 8 ? 5 ? ?
? ? ? ? ? ? 2 ? ?
? ? ? ? 4 5 ? 7 8

To run this example problem through the Sudoku example, use the following command:

yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar sudoku /usr/hdp/*/hadoop/src/hadoop-mapreduce-project/hadoop-mapreduce-examples/src/main/java/org/apache/hadoop/examples/dancing/puzzle1.dta

The results appear similar to the following text:

8 5 1 3 9 2 6 4 7
4 3 2 6 7 8 1 9 5
7 9 6 5 1 4 3 8 2
6 1 4 8 2 3 7 5 9
5 7 8 9 6 1 4 2 3
3 2 9 4 5 7 8 1 6
9 4 7 2 8 6 5 3 1
1 8 5 7 3 9 2 6 4
2 6 3 1 4 5 9 7 8

Pi (π) example

The pi sample uses a statistical (quasi-Monte Carlo) method to estimate the value of pi. Points are placed at random in a unit square. The square also contains a circle. The probability that the points fall within the circle is equal to the area of the circle, pi/4. The value of pi can be estimated from the value of 4R. R is the ratio of the number of points that are inside the circle to the total number of points that are within the square. The larger the sample of points used, the better the estimate is.

Use the following command to run this sample. This command uses 16 maps with 10,000,000 samples each to estimate the value of pi:

yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar pi 16 10000000

The value returned by this command is similar to 3.14159155000000000000. For references, the first 10 decimal places of pi are 3.1415926535.

10-GB GraySort example

GraySort is a benchmark sort. The metric is the sort rate (TB/minute) that is achieved while sorting large amounts of data, usually a 100 TB minimum.

This sample uses a modest 10 GB of data so that it can be run relatively quickly. It uses the MapReduce applications developed by Owen O'Malley and Arun Murthy. These applications won the annual general-purpose ("Daytona") terabyte sort benchmark in 2009, with a rate of 0.578 TB/min (100 TB in 173 minutes). For more information on this and other sorting benchmarks, see the Sort Benchmark site.

This sample uses three sets of MapReduce programs:

TeraGen: A MapReduce program that generates rows of data to sort
TeraSort: Samples the input data and uses MapReduce to sort the data into a total order

TeraSort is a standard MapReduce sort, except for a custom partitioner. The partitioner uses a sorted list of N-1 sampled keys that define the key range for each reduce. In particular, all keys such that sample[i-1] <= key < sample[i] are sent to reduce i. This partitioner guarantees that the outputs of reduce i are all less than the output of reduce i+1.
TeraValidate: A MapReduce program that validates that the output is globally sorted

It creates one map per file in the output directory, and each map ensures that each key is less than or equal to the previous one. The map function generates records of the first and last keys of each file. The reduce function ensures that the first key of file i is greater than the last key of file i-1. Any problems are reported as an output of the reduce phase, with the keys that are out of order.

Use the following steps to generate data, sort, and then validate the output:

Generate 10 GB of data, which is stored to the HDInsight cluster's default storage at /example/data/10GB-sort-input:
```
yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar teragen -Dmapred.map.tasks=50 100000000 /example/data/10GB-sort-input
```
The -Dmapred.map.tasks tells Hadoop how many map tasks to use for this job. The final two parameters instruct the job to create 10 GB of data and to store it at /example/data/10GB-sort-input.

Use the following command to sort the data:

yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar terasort -Dmapred.map.tasks=50 -Dmapred.reduce.tasks=25 /example/data/10GB-sort-input /example/data/10GB-sort-output

The -Dmapred.reduce.tasks tells Hadoop how many reduce tasks to use for the job. The final two parameters are just the input and output locations for data.

Use the following to validate the data generated by the sort:

yarn jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-mapreduce-examples.jar teravalidate -Dmapred.map.tasks=50 -Dmapred.reduce.tasks=25 /example/data/10GB-sort-output /example/data/10GB-sort-validate

Next steps

From this article, you learned how to run the samples included with the Linux-based HDInsight clusters. For tutorials about using Pig, Hive, and MapReduce with HDInsight, see the following topics:

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